Methods, systems, articles of manufacture and apparatus to monitor auditing devices

ABSTRACT

Methods, apparatus, systems and articles of manufacture are disclosed to monitor auditing devices. An example apparatus includes a workload analyzer to obtain alert data related to a potential new product from an auditing device, and a product analyzer to identify a product within the alert data to determine if the product has been previously identified by another auditing device. In response to determining that the product has not been previously identified, the example apparatus includes an alert analyzer to cluster the alert data based on characteristics associated with an auditor profile of the auditing device, and determine a probability of transmitting the alert data to other auditing devices based on the clustered alert data. The example apparatus also includes an alert authorizer to suppress the alert data from being transmitted to the other auditing devices to reduce an amount of network resources required for subsequent processing when the probability does not satisfy a threshold.

FIELD OF THE DISCLOSURE

This disclosure relates generally to product auditing, and, moreparticularly, to methods, systems, articles of manufacture and apparatusto monitor auditing devices.

BACKGROUND

Employees (e.g., auditors) of auditing entities visit stores and collectinformation about products in the stores. The auditors collectinformation such as the price of a product and the number of units ofthe product available in a store. The information from the auditors isused to generate reports that are provided to clients of the auditingentities.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of an example environment in which anexample auditing device constructed in accordance with the teachings ofthis disclosure is to perform auditing tasks.

FIG. 2 is a schematic illustration of an example implementation of theauditing device of FIG. 1.

FIG. 3 is a schematic illustration of an example implementation of thecentral server of FIG. 1.

FIG. 4 is a flowchart representative of example machine-readableinstructions that may be executed by the example auditing device ofFIGS. 1 and/or 2.

FIG. 5 is a flowchart representative of example machine readableinstructions that may be executed by the example central server of FIGS.1 and/or 3.

FIG. 6 is a block diagram of an example processing platform structuredto execute the example machine-readable instructions of FIG. 4 toimplement the example auditing device of FIGS. 1 and/or 2.

FIG. 7 is a block diagram of an example processing platform structuredto execute the example machine-readable instructions of FIG. 5 toimplement the example central server of FIGS. 1 and/or 3.

The figures are not to scale. In general, the same reference numberswill be used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts.

DETAILED DESCRIPTION

Product manufacturers, markets, distributors, and others (e.g., clients)wish to track and research how products are made available and sold in amarket of interest (e.g., stores, merchants, retailers, etc., generallyreferred to herein as “stores”). For example, a soft drink manufacturerclient to track the circumstances related to sales of their productsand/or other products available on the market at one or more stores in aregion. As such, the client may request an audit be performed in aparticular location (e.g., a grocery store, a department store, abuilding supply store, a warehouse, a food pantry, a purchasing clubs(e.g., Costco®), etc.). The request may include information about anynumber of products that is desired to be audited. For example, clientsmay request information about the location of products in stores, thenumber of products in stores, the number of products in facings (e.g.,the number of products displayed at the front of a shelf) in stores, theprice of products in stores, the existence of promotional pricing instores, the type of exhibition of products (e.g., in a basket, on an endcap, on an island, in an aisle, etc.), etc. The request of the clientsmay specify a single product, multiple products from aproducer/manufacturer, multiple products of a particular type (e.g.,products in the soft drink category), etc. The request may also specifya geographical region, particular stores, and/or any other type(s) ofinformation about the areas from which the information should begathered to satisfy the request. The request may specify any level ofgranularity such as, for example, information about stock-keeping unit(SKU) numbers, information about products by product regardless of theproduct size (e.g., grouping 10 ounce, 12 ounce, 16 ounce, and 24 ouncesizes together), information about products by producer/manufacturer(e.g., grouping all products from a particular producer/manufacturer),etc.

As such, an auditing entity may receive the requests from the client dgenerate workloads which are subsequently distributed to auditors forperforming an audit to collect images, and facts and/or data aboutproducts. As used herein, “workload” may be any type of instruction tocollect information including instructions to collect an image of aproduct, collect an image of a shelf (e.g., a shelf of products of aspecified type), collect a fact (e.g., Input a price. Input a location.Input a number of products), an instruction to input an opinion (e.g.,Identify the product most prominently displayed?), an interrogativesentence (e.g., What is the product nearest the entrance?), a batch ofdata collection tasks etc.

In some examples, the auditors are agents of the auditing entity andvisit the stores and perform workloads requested by the clients. Theauditors utilize handheld computing devices (e.g., auditing devices) toperform the workloads while visiting the stores, and to wirelesslytransmit information (e.g., images, facts, opinions, etc.) to a centralserver discovered while performing the workloads. The auditors are alsoconnected to other auditors via an auditor social network. The auditorsmay communicate with one another regarding certain request in one ormore workloads, new products discovered, updated locations ofadvertisements, etc. In addition, auditors may generate messages oralerts which may be transmitted to other auditors regarding new productsor other information of interests to auditors. However, these messagesand alerts are often sent repetitively (e.g., the same message or alertis sent by multiple auditors), which causes undue hardship on aprocessor of the auditing devices and/or a central server, and causesauditors to re-audit workloads which have already been performed.

Examples disclosed herein analyze messages and alert data from auditorsto determine if the messages and alert data are to be suppressed toreduce an amount of network, computational and/or personnel resourcesrequired for auditing activities. For example, an auditor may identify anew product while performing a workload. The auditor may take a pictureof the potentially new product and submit it to a central server to betransmitted to other auditors. The example central server may analyzethe potential new product and determine that the product has beenpreviously identified. In the event the product has been previouslyidentified, then further personnel efforts to locate the product duringone or more subsequent auditing tasks may be wasteful. Similarly, in theevent the product has been previously identified and one or more otherauditors continue to generate audit entry data for that product, thensuch additional audit entry data is duplicative, thereby wastingcomputational resources to store, wasting storage resources, and wastingnetwork bandwidth. As such, the central server may suppress the alertdata to reduce the amount of resources (e.g., network, computer,storage, etc.) required for transmitting such an alert to otherauditors.

In some examples, the central server 104 may determine that the producthas not been previously identified. For example, the product does notexists in a product reference image database. As such, the centralserver 104 may cluster the alert data based on characteristicsassociated with the auditor. The central server 104 clusters based oncharacteristics of auditors because some auditors may per form betterthan others (e.g., more efficient, more accurate, etc.). As such,clustering based on auditor characteristics increases a computationalefficiency of the central server 104 by identifying characteristics ofauditors that are more reliable. In some examples, the characteristicsmay include a skill level of the auditor and/or a rate of false alertdata (e.g., a majority of the auditor's alerts are suppressed). Theexample central server 104 may determine a probability of transmittingthe alert data to other auditors based on performing a logisticregression, which results in a probability indicative of a degree ofreliability and/or accuracy from the auditor, for example. The resultingprobability is compared to a threshold and the central serversubsequently determines whether to transmit the alert data or suppressthe alert data to reduce an amount of network resources required forsubsequent processing, for example.

FIG. 1 is a block diagram of an example environment 100 in which anexample auditing device 102, constructed in accordance with theteachings of this disclosure, operates to execute workloads in a store.In the illustrated example of FIG. 1, the auditing device 102 is asmartphone. However, in other examples, the auditing device 102 can be,for example, a mobile device, a tablet, a laptop computer, and/or anyother suitable device. The example auditing device 102 of FIG. 1 isdescribed in further detail in connection with FIG. 2 below.

The example environment 100 of FIG. 1 includes an example central server104 commutatively coupled to the auditing device 102 to synchronizeand/or process information corresponding to the auditing device 102. Insome examples, the example central server 104 communicates with theauditing device 102 via a wireless Internet network. Additionally oralternatively, in some examples, the central server 104 communicateswith the auditing device 102 using any other suitable communicationprotocol, including but not limited to, a cellular network, a datanetwork, Bluetooth, Radio-Frequency Identification (RFID), Near FieldCommunication (NFC), a wired Internet connection, etc. In some examples,audit data and/or results are communicated between the central server104 and the auditing device 102. For example, the central server 104transmits workloads to the auditing device 102. In some examples, theauditing device 102 transmits reported results (e.g., image-basedresults and/or fact-based results) to the central server 104.

In some examples, the illustrated environment 100 of FIG. 1 includes animage database 106. In some examples, the central server 104 is incommunication with the image database 106 via a wired and/or wirelessnetwork. In some examples, the central server 104 synchronizes dataand/or images between the example image database 106 and the exampleauditing device 102. Additionally or alternatively, in some examples,the auditing device 102 is in direct communication with the imagedatabase 106. In some examples, the auditing device 102 transmitsreported image-based results and/or point of sale images to the centralserver 104 and/or the image database 106. In some such examples, thecentral server 104 communicates the image-based results and/or point ofsale images from a workload to the central server 104 and/or the imagedatabase 106. In some examples, the auditing device 102 transmits theimage-based results and/or the point of sale images in response toobtaining the image-based results and/or the point of sale images whileexecuting a workload. In other examples, the auditing device 102 delaystransmittal of the image-based results and/or the point of sale imagesuntil the auditing device 102 is in communication with the centralserver 104 via a network connection (e.g., such as a wireless and/orwired Internet connection In some examples, the image database 106transmits point of sale images to the auditing device 102 and/or thecentral server 104. In some examples, the image database 106 is incommunication with the central server 104, and/or the auditing device102 via any wired or wireless connection. In some examples, the exampleimage database 106 is implemented by a server. Additionally oralternatively, the image database 106 can be implemented by, forexample, a mass storage device, such as a hard drive, a flash disk, aflash drive, etc.

FIG. 2 is a block diagram of an example implementation of the auditingdevice 102 of FIG. 1. In the illustrated example of FIG. 2, the auditingdevice 102 includes an example auditing device processor 202 structuredto enable the auditing device 102 to execute a workload interactively.In some such examples, the auditing device processor 202 is operativelycoupled to additional components/structure of the auditing device 102,such as an example camera 204, an example display 206, and/or aninput/output (I/O) and/or other communication interface 208 via a bus.

In the illustrated example of FIG. 2, the auditing device 102 includesthe example camera 204 operatively coupled to the auditing deviceprocessor 202. In some examples, the camera 204 captures images) andcommunicates the image to the auditing device processor 202. In someexamples, the camera 204 is capable of scanning barcodes to provideadditional input related to products in the image(s), and maycommunicate the barcodes to the auditing device processor 202.

The example auditing device 102 of FIG. 2 includes the example display206 operatively coupled to the auditing device processor 202. Thedisplay 206, in some examples, presents instructions from a workloadand/or results subsequent execution of a workload to the auditor via auser interface (e.g., an interactive and/or graphical user interface)implemented by the example auditing device processor 202 of the auditingdevice 102. In some examples, the display 206 is a touchscreen tosimplify interaction between the auditing device 102 and the auditorwhen providing input related to the displayed results. In some examples,the auditor provides input in response to prompts on the display 206communicated via the user interface. In some examples, the auditorprovides input to correct errors in the results of executing a workloadpresented to the auditor on the display 206 via the user interface.

In some examples, the auditing device 102 includes the exampleinput/output (I/O) interface 208 operatively coupled to the processor202. The I/O interface 208 is operative to communicate with, in someexamples, the central server 104, and/or the image database 106 ofFIG. 1. In some examples, the I/O interface 208 is operative tointeractively communicate with the auditor using, for example, thedisplay 206, a button on the auditing device, a voice command, agesture, a sensor to receive input from the auditor, etc. In some suchexamples, the I/O interface 208 enables the auditor to provide input tothe user interface, via the display 206, related to image(s), workloads,and/or the results displayed to the auditor.

An example implementation of the auditing device processor 202 of theexample auditing device 102 is also depicted in FIG. 2. In someexamples, the example auditing device processor 202 of the auditingdevice 102 includes an example workload analyzer 210, an example imageanalyzer 212, an example product identifier 214, an example resultsanalyzer 216, an example message generator 218, and an example alertgenerator 220, which communicate via a bus. In some examples, theworkload analyzer 210 is a means for analyzing a workload, or a workloadanalyzing means. In some examples, the image analyzer 212 is a means foranalyzing an image, or an image analyzing means. In some examples, theproduct identifier 214 is a means for identifying a product, or aproduct identifying means. In some examples, the results analyzer 216 isa means for analyzing results, or a results analyzing means. In someexamples, the message generator 218 is a means for generating a message,or a message generating means. In some examples, the alert generator 220is a means for generating an alert, or an alert generating means.

In the illustrated example of FIG. 2, the workload analyzer 210 receivesand/or retrieves workloads from the central server 104 relating to tasksthat are to be completed during an audit. The example workload analyzer210 analyzes the workload information to determine which tasks are to becompleted. For example, the workload analyzer 210 retrieves a workloadthat includes tasks to obtain images of certain products as well asanalyze product advertisement displays within a store. As such, theexample workload analyzer 210 determines that the tasks related to theproduct advertisement displays are to be completed first based on aworkload task hierarchy (e.g., product displays are to be completed toprior to obtaining images). The example workload analyzer 210subsequently displays the product advertisement display tasks to anauditor via the display 206 so the auditor may perform the tasks of theworkload. Additionally, the example workload analyzer 210 retrievesmessages and alert data from the central server 104 pertaining to anupdated task that is to be completed. For example, another auditor mayidentify a new product that was not previously identified in a workloadfor a specific geographic region. As such, the auditor may send amessage and/or an alert to other auditors within that geographic regionvia the central server 104 so that the other auditor's workloads can beupdated to collect such information to avoid having to re-audit the samestore/products. The example workload analyzer 210 of FIG. 2 maintains arecord of the tasks and when they are completed. For example, theworkload analyzer 210 displays a task for an auditor to perform (e.g.,take a photo of a certain product) and retrieves a response (e.g., viainput on the display 206 and/or upload of an image) from the auditor.The example workload analyzer 210 records that task as complete anddisplays one or more tasks from the workload for the auditor tocomplete. The example workload analyzer 210 continues this process untilthe entire workload(s) has/have been executed and/or otherwiseperformed.

When the workload includes image based results, the example imageanalyzer 212 analyzes the images to determine the products in the image.For example, a workload may contain a task to obtain an image relatingto a product of interest. The auditor may obtain the image based onprompts from the example workload analyzer 210, and the example imageanalyzer 212 subsequently analyzes the image to determine if the imageis of the correct product. In some examples, the image analyzer 212accesses the image database 106 to determine if a match exists (e.g.,the image matches an image of a product in the image database 106). Insome examples, the image analyzer 212 identifies the product in theimage based on a match in the image database and transmits an indicationof the product to the product identifier 214 and/or the results analyzer216 for further processing. In some examples, the indication may includea name of the product if the product was identified in the imagedatabase 106, or an indication of an unknown product if the product inthe image was not identified.

The results from the example workload analyzer 210 and the example imageanalyzer 212 are transmitted to the example product identifier 214 forfurther analysis. The example product identifier 214 of FIG. 2identifies products that were scheduled and/or otherwise planned to beidentified in the workload from the workload analyzer 210 as well asproducts that were scheduled and/or planned to be identified in theimages from the image analyzer 212. For example, the product identifier214 determines that product A was scheduled and/or planned to beidentified (e.g., from a task of the workload), but an indication fromthe image analyzer 212 identifies product B. The example productidentifier 214 marks an error for that specific task and proceeds toanalyze other tasks within the workload in a similar manner. In someexamples, an auditor identifies a new product while executing a workloadand obtains an image of the new product. In this example, the imageanalyzer 212 provides an indication of unknown (e.g., metadata) alongwith the image because the product may not yet be in the image database106 (e.g., a reference image of the product has not been generated). Assuch, the example product identifier 214 flags the image and/or the taskas message or alert ready (e.g., provide an indication to the messagegenerator 218 and/or the alert generator 220 to generate an alert totransmit to other auditors).

Following the identification of the products, the example resultsanalyzer 216 determines a success rate associated with a workload ofinterest (e.g., the workload currently being analyzed). For example, theresults analyzer 216 analyzes each task and determines if each task wascompleted successfully or whether an error occurred (e.g., the exampleproduct identifier 214 identified that the wrong product image wasobtained). The example results analyzer 216 subsequently calculates thesuccess rate (e.g., 70/100=70% tasks completed successfully) anddetermines if the success rate meets a success rate threshold (e.g.,90%, 85%, 70%, etc.). In some examples, the results analyzer 216 promptsan auditor to re-audit the tasks for which an error occurred before theauditor leaves the store to obtain a 100% success rate. Alternatively,the example results analyzer 216 determines that the success rate waswithin a threshold and may not prompt the auditor to re-audit the tasksthat received an error. The example results analyzer 216 generates areport associated with the workload(s) and the auditor that executed theworkload(s), which includes the success rate so that metrics (e.g., timeto execute workload, success rate of workload, etc.) may be determinedfor the auditor at the central server 104.

The illustrated example of FIG. 2 includes the message generator 218 togenerate messages for transmission to other auditing devices 102. Forexample, when an auditor identifies a new product, the message generator218 generates a message which is subsequently transmitted to otherauditors via the central server 104. The example message generator 218includes an image of the product which was obtained by an auditor of theauditing device 102 and/or any text that auditor includes product nameform label, a question to see if any other auditors have identified thisproduct, etc.). The example message generator 218 is utilized togenerate any type of message that an auditor desires to send to otherauditing devices 102 within an auditing device 102 network (e.g., ageographic region, all auditing devices for a particular skill level,etc.).

The example alert generator 220 generates alert data to alert otherauditors that a new product has been identified and needs to be auditedprior to the auditor completing the auditor's workload. Suchtransmission of alert data allows auditors to obtain audit data withoutrequiring another auditor to re-audit the store, thus saving money andresources for auditing entities. In the illustrated example of FIG. 2,the alert generator 20 retrieves information regarding potential alertsfrom the workload analyzer 210, the image analyzer 212, the productidentifier 214, the results analyzer 216, and/or the message generator218. For example, the alert generator 220 retrieves an indication fromthe product identifier 214 that a new product has been identified andneeds to be appended to workloads in a geographic area. The examplealert generator 220 subsequently generates alert data which isre-directed to the other auditing devices 102 via the central server104. The alert data is re-directed to the other auditing devices via thecentral server 104 to avoid multiple alerts being transmitted about thesame product, which saves network resources, storage space, and reducesprocessing cycles.

An example implementation of the central server 104 is illustrated inFIG. 3. In some examples, the example central server 104 includes anexample workload authorizer 302, an example workload analyzer 304, andexample product analyzer 306, an example message analyzer 308, anexample message database 310, an example alert analyzer 312, an examplealert database 314, an example auditor profile database 316, an examplealert authorizer 318, and an example product reference generator 320,which communicate via a bus. In some examples, the workload authorizer302 is a means for authorizing a workload, or a workload authorizingmeans. In some examples, the workload analyzer 304 is a means foranalyzing a workload, or a workload analyzing means. In some examples,the product analyzer 306 is a means for analyzing a product, or aproduct analyzing means. In some examples, the message analyzer 308 is ameans for analyzing a message, or a message analyzing means. In someexamples, the alert analyzer 312 is a means for analyzing an alert, oran alert analyzing means. In some examples, the alert authorizer 318 isa means for authorizing an alert, or an alert authorizing means. In someexamples, the product reference generator 320 is a means for generatinga product reference, or a product reference generating means.

In the illustrated example of FIG. 3, the workload authorizer 302generates workloads for completion by auditors based on requests fromclients. For example, the workload authorizer 302 receives and/orotherwise retrieves requests to audit a product of interest. The exampleworkload authorizer 302 generates a workload including tasks associatedwith the product of interest. In some examples, the workload authorizer302 obtains requests from a client that exceeds a workload threshold(e.g., cannot be performed because not enough hours in the day, too farfor auditor to travel, too many audit stops in view of auditorefficiency metrics, etc.). As such, the workload authorizer 302 maysuppress the requests and not generate a workload. In some examples, theworkload authorizer 302 authorizes workloads based on characteristicsassociated with an auditor. For example, the workload authorizer 302 mayaccess the auditor profile database 316 and determine a skill levelassociated with an auditor and suppress any tasks on a workload that arenot within the auditors skill level. The workload authorizer 302transmits the workloads to the auditing devices 102 for execution.

The example workload analyzer 304 obtains reports from the resultsanalyzer 304 regarding a success rate of a certain workload. The exampleworkload analyzer 304 analyzes the report and updates an auditor profilethat executed the workload in the auditor profile database 316. Forexample, the workload analyzer 304 may identify the auditor associatedwith the executed workload, obtain the auditor profile form the auditorprofile database 316, and update metrics (e.g., time to execute audit,success rate, etc. associated with the auditor profile. The exampleworkload analyzer 304 may also obtain alert data related to a potentialnew product from an auditing device 102. In some examples, the workloadanalyzer 304 determines if the product in the alert data is associatedwith another workload of another auditing device 102. The workloadanalyzer 304 of the illustrated example may transmit a determination tothe alert authorizer 318 to suppress the alert data from beingtransmitted if the workload analyzer 304 determines that anotherauditing device 102 is executing a workload that includes the productidentified in the alert data. In some examples, the workload analyzer304 may determine that the product in the alert data is not associatedwith another workload and may transmit a determination to the productanalyzer 306.

The example product analyzer 306 receives and/or otherwise retrieves thedetermination from the workload analyzer 304 that the product in thealert data is not associated with another workload. In some examples,when the alert data from the auditing device 102 includes an image, theproduct analyzer 306 accesses the image database 106 to determine if aproduct reference image matches the image of the product in the alertdata. If the example product analyzer 306 identifies a match (e.g., aproduct that has already been identified), the product analyzer 306transmits a determination to the alert authorizer 318 to suppress thealert data from being transmitted to the other auditing devices 102. Inexamples where the alert data does not include an image and onlyincludes a message (e.g., name of a label on product), the productanalyzer 306 may transmit the alert data to the message analyzer 308 forfurther processing.

The message analyzer 308 of the illustrated example parses messages fromother auditing devices to determine if the product in the alert data hasbeen previously identified. For example, the message analyzer 308 mayanalyze messages in the message database 310 to determine if any othermessages include a product name similar to the product identified in thealert data. The example message analyzer 308 may utilize any type oftext recognition algorithm to identify potential matches. In someexamples, the message analyzer 308 identifies a match within the messagedatabase 310 and transmits an indication message to the product analyzer306 indicating that the product has been identified in the messages fromother auditing devices 102. In some examples, the message analyzer 308may not identify the product within the message database 310 and maytransmit an indication to the alert analyzer 312.

The example alert analyzer 312 analyzes the alert data to determinewhether or not to suppress the alert data or transmit the alert data toother auditing devices 102. The example alert analyzer 312 accessesother alert data within the alert database 314 to determine if the alertdata has been previously transmitted to the auditing devices 102. If theexample alert analyzer 312 determines that the alert data has beenpreviously transmitted to the other auditing devices 102, the alertanalyzer 312 transmits an indication message to the example alertauthorizer 318 to suppress the alert data. The example alert analyzer312 further analyzes the alert data if no match was identified in thealert database 314. The alert analyzer 312 of the illustrated exampleclusters the alert data based on characteristics associated with anauditor profile of the auditing device 102. In some examples, the alertanalyzer 312 accesses an auditor profile from the auditor profiledatabase 316 and accesses characteristics associated with the auditorprofile, which may include a number of years of experience, a skilllevel, an efficiency measure, an average amount of time to perform anaudit, an alert data generation measure, a number of stock keeping units(SKU), etc. The example alert analyzer 312 may determine a cluster index(i) for each characteristic associated with the auditor profile (e.g.,cluster 1 is skill level, cluster index 2 is an efficiency measure,etc.). The alert analyzer 312 performs a logistic regression to learncoefficients (a(i,m)) associated with a cluster index (i) to determine aprobability (p(i)) of transmitting the alert data for a specific clusterindex (i). In some examples, the alert analyzer 312 performs thelogistic regression in a manner consistent with example Equation 1.Additionally, the alert analyzer 312 determines the probability oftransmitting the alert data for a cluster index (i) where x representsaudit variables (e.g., audit time, total number of SKUs, etc.) in amanner consistent with example Equation 2.Y(i)=a(i,1)+(a(i,2)x2+ . . . +a(i,m)x(m)  Equation 1:p(i)=exp(Y(i)/[1+exp(Y(i))]  Equation 2:

The alert analyzer 312 obtains the resulting probabilities (p(i)) foreach cluster index (i) and transmits the resulting probabilities foreach cluster to the alert authorizer 318 for further processing.

The example alert authorizer 318 obtains the probabilities (p(i)) foreach cluster index (i) and determines whether the probabilities satisfya threshold. The example alert authorizer 318 compares each probabilityto a probability threshold (e.g., 70%, 80%, 90%, etc.) and determineswhether to suppress or transmit the alert data to other auditing devices102 to reduce an amount of network resources required for subsequentprocessing. In some examples, the alert authorizer 318 determines thatthe probability associated with a skill level index satisfies athreshold, but the probability associated with an efficiency measuredoes not satisfy the threshold. As such, the example alert authorizer318 transmits the alert data to other auditing devices 102 associatedwith the skill level index (e.g., 1 year of experience), and suppressesthe alert data from being transmitted to other auditing devicesassociated with the efficiency measure index to reduce an amount ofnetwork resources required for subsequent processing of transmitting thealert data to the other auditing devices 102.

The example product reference generator 320 utilizes images from thealert data to generate a product reference image that is subsequentlystored in the image database 106. For example, when alert data includesan image, and satisfies the probability threshold of the alertauthorizer 318, the product reference generator 320 generates a productreference image which includes the image from the alert data and aproduct name from the alert data. In some examples, the productreference generator 320 prompts an auditor via the display 206 to enterand/or verify a product name for the image prior to generating theproduct reference image. In some examples, the product reference imageis authorized by a manager of the central server 104.

While an example manner of implementing the auditing device 102 of FIG.1 is illustrated in FIGS. 1 and 2, one or more of the elements,processes and/or devices illustrated in FIGS. 1 and 2 may be combined,divided, re-arranged, omitted, eliminated and/or implemented in anyother way. Further, the example workload analyzer 210, the example imageanalyzer 212, the example product identifier 214, the example resultsanalyzer 216, the example message generator 218, the example alertgenerator 220 and/or, more generally, the example auditing device 102 ofFIG. 1 may be implemented by hardware, software, firmware and/or anycombination of hardware, software and/or firmware. Thus, for example,any of the example workload analyzer 210, the example image analyzer212, the example product identifier 214, the example results analyzer216, the example message generator 218, the example alert generator 220and/or, more generally, the example auditing device 102 of FIG. 1 couldbe implemented by one or more analog or digital circuit(s), logiccircuits, programmable processor(s), programmable controller(s),graphics processing unit(s) (GPU(s)), digital signal processor(s)(DSP(s)), application specific integrated circuit(s) (ASIC(s)),programmable logic device(s) (PLD(s)) and/or field programmable logicdevice(s) (FPLD(s)). When reading any of the apparatus or system claimsof this patent to cover a purely software and/or firmwareimplementation, at least one of the example workload analyzer 210, theexample image analyzer 212, the example product identifier 214, theexample results analyzer 216, the example message generator 218, and theexample alert generator 220 is/are hereby expressly defined to include anon-transitory computer readable storage device or storage disk such asa memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-raydisk, etc. including the software and/or firmware. Further still, theexample auditing device 102 of FIGS. 1 and 2 may include one or moreelements, processes and/or devices in addition to, or instead of, thoseillustrated in FIGS. 1 and 2, and/or may include more than one of any orall of the illustrated elements, processes and devices. As used herein,the phrase “in communication,” including variations thereof, encompassesdirect communication and/or indirect communication through one or moreintermediary components, and does not require direct physical (e.g.,wired) communication and/or constant communication, but ratheradditionally includes selective communication at periodic intervals,scheduled intervals, aperiodic intervals, and/or one-time events.

A flowchart representative of example hardware logic, machine readableinstructions, hardware implemented state machines, and/or anycombination thereof for implementing the auditing device 102 is shown inFIG. 4. The machine readable instructions may be an executable programor portion of an executable program for execution by a computerprocessor such as the processor 612 shown in the example processorplatform 600 discussed below in connection with FIG. 6. The program maybe embodied in software stored on a non-transitory computer readablestorage medium such as a CD-ROM, a floppy disk, a hard drive, a DVD, aBlu-ray disk, or a memory associated with the processor 612, but theentire program and/or parts thereof could alternatively be executed by adevice other than the processor 612 and/or embodied in firmware ordedicated hardware. Further, although the example program is describedwith reference to the flowchart illustrated in FIG. 4, many othermethods of implementing the example auditing device 102 mayalternatively be used. For example, the order of execution of the blocksmay be changed, and/or some of the blocks described may be changed,eliminated, or combined. Additionally or alternatively, any or all ofthe blocks may be implemented by one or more hardware circuits (e.g.,discrete and/or integrated analog and/or digital circuitry, an FPGA, anASIC, a comparator, an operational-amplifier (op-amp), a logic circuit,etc.) structured to perform the corresponding operation withoutexecuting software or firmware.

As mentioned above, the example processes of FIG. 4 may be implementedusing executable instructions (e.g., computer and/or machine readableinstructions) stored on a non-transitory computer and/or machinereadable medium such as a hard disk drive, a flash memory, a read-onlymemory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media.

Example machine readable instructions that may be executed to implementthe auditing device 102 of FIGS. 1 and/or 2 are illustrated in FIG. 4.With reference by example to FIG. 2, the example machine readableinstructions (e.g., process 400) of FIG. 4 begin when the exampleworkload analyzer 210 obtains a workload from the central server 104(block 402). The example workload analyzer 210 initiates an auditor toperform the workload. The example image analyzer 212 analyzes imagesrelated to products from the workload (block 404). For example, theimage analyzer 212 accesses images obtained from the camera 204 andaccess images from the image database 106 to identify a referenceproduct image match. The example product identifier 214 identifiesproducts to confirm completion of the workload (block 406). The exampleproduct identifier 214 determines if all the products from the workloadhave been identified (block 408). If the product identifier 214determines that not all of the products have been identified (block408), the example process 400 returns to block 404. If the exampleproduct identifier 214 determines that all products in the workload havebeen identified (block 408), the product identifier 214 determines ifany new products have been identified (block 410). If the productidentifier 214 determines that no new products have been identified(block 410), the example process 400 ends. If the example productidentifier 214 determines that a new product has been identified (block410), the message generator 218 generates a message indicating a newproduct identified (block 412). The example alert generator 220generates an alert indicating the new product has been identified (block414), and transmits the message and alert data to the central server 104(block 416). In some examples, the process 400 ends until anotherworkload is to be processed.

FIG. 6 is a block diagram of an example processor platform 600structured to execute the instructions of FIG. 4 to implement theauditing device 102 of FIGS. 1 and/or 2. The processor platform 600 canbe, for example, a server, a personal computer, a workstation, aself-learning machine (e.g., a neural network), a mobile device (e.g., acell phone, a smart phone, a tablet such as an iPad™), a personaldigital assistant (PDA), an Internet appliance, a DVD player, a CDplayer, a digital video recorder, a Blu-ray player, a gaming console, apersonal video recorder, a set top box, a headset or other wearabledevice, or any other type of computing device.

The processor platform 600 of the illustrated example includes aprocessor 612. The processor 612 of the illustrated example is hardware.For example, the processor 612 can be implemented by one or moreintegrated circuits, logic circuits, microprocessors, GPUs, DSPs, orcontrollers from any desired family or manufacturer. The hardwareprocessor may be a semiconductor based (e.g., silicon based) device. Inthis example, the processor 600 implements the example workload analyzer210, the example image analyzer 212, the example product identifier 214,the example results analyzer 216, the example message generator 218, andthe example alert generator 220.

The processor 612 of the illustrated example includes a local memory 613(e.g., a cache). The processor 612 of the illustrated example is incommunication with a main memory including a volatile memory 614 and anon-volatile memory 616 via a bus 618. The volatile memory 614 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS® Dynamic Random Access Memory(RDRAM®) and/or any other type of random access memory device. Thenon-volatile memory 616 may be implemented by flash memory and/or anyother desired type of memory device. Access to the main memory 614, 616is controlled by a memory controller.

The processor platform 600 of the illustrated example also includes aninterface circuit 620. The interface circuit 620 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), a Bluetooth® interface, a near fieldcommunication (NFC) interface, and/or a PCI express interface.

In the illustrated example, one or more input devices 622 are connectedto the interface circuit 620. The input device(s) 622 permit(s) a userto enter data and/or commands into the processor 612. The inputdevice(s) can be implemented by, for example, an audio sensor, amicrophone, a camera (still or video), a keyboard, a button, a mouse, atouchscreen, a track-pad, a trackball, isopoint and/or a voicerecognition system.

One or more output devices 624 are also connected to the interfacecircuit 620 of the illustrated example. The output devices 624 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay (LCD), a cathode ray tube display (CRT), an in-place switching(IPS) display, a touchscreen, etc.), a tactile output device, a printerand/or speaker. The interface circuit 620 of the illustrated example,thus, typically includes a graphics driver card, a graphics driver chipand/or a graphics driver processor.

The interface circuit 620 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem, a residential gateway, a wireless access point, and/or a networkinterface to facilitate exchange of data with external machines (e.g.,computing devices of any kind) via a network 626. The communication canbe via, for example, an Ethernet connection, a digital subscriber line(DSL) connection, a telephone line connection, a coaxial cable system, asatellite system, a line-of-site wireless system, a cellular telephonesystem, etc.

The processor platform 600 of the illustrated example also includes oneor more mass storage devices 628 for storing software and/or data.Examples of such mass storage devices 628 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, redundantarray of independent disks (RAID) systems, and digital versatile disk(DVD) drives.

The machine executable instructions 632 of FIG. 4 may be stored in themass storage device 628, in the volatile memory 614, in the non-volatilememory 616, and/or on a removable non-transitory computer readablestorage medium such as a CD or DVD.

While an example manner of implementing the central server 104 of FIG. 1is illustrated in FIGS. 1 and 3, one or more of the elements, processesand/or devices illustrated in FIGS. 1 and 3 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example workload authorizer 302, the example workloadanalyzer 304, the example product analyzer 306, the example messageanalyzer 308, the example alert analyzer 312, the example alertauthorizer 318, the example product reference generator 320 and/or, moregenerally, the example central server 104 of FIGS. 1 and 3 may beimplemented by hardware, software, firmware and/or any combination ofhardware, software and/or firmware. Thus, for example, any of theexample workload authorizer 302, the example workload analyzer 304, theexample product analyzer 306, the example message analyzer 308, theexample alert analyzer 312, the example alert authorizer 318, theexample product reference generator 320 and/or, more generally, theexample central server 104 of FIGS. 1 and 3 could be implemented by oneor more analog or digital circuit(s), logic circuits, programmableprocessor(s), programmable controller(s), graphics processing unit(s)(GPU(s)), digital signal processor(s) (DSP(s)), application specificintegrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s))and/or field programmable logic device(s) (FPLD(s)). When reading any ofthe apparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the example workloadauthorizer 302, the example workload analyzer 304, the example productanalyzer 306, the example message analyzer 308, the example alertanalyzer 312, the example alert authorizer 318, and the example productreference generator 320 is/are hereby expressly defined to include anon-transitory computer readable storage device or storage disk such asa memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-raydisk, etc. including the software and/or firmware. Further still, theexample central server 104 of FIGS. 1 and 3 may include one or moreelements, processes and/or devices in addition to, or instead of, thoseillustrated in FIGS. 1 and 3, and/or may include more than one of any orall of the illustrated elements, processes and devices. As used herein,the phrase “in communication,” including variations thereof, encompassesdirect communication and/or indirect communication through one or moreintermediary components, and does not require direct physical (e.g.,wired) communication and/or constant communication, but ratheradditionally includes selective communication at periodic intervals,scheduled intervals, aperiodic intervals, and/or one-time events.

A flowchart representative of example hardware logic, machine readableinstructions, hardware implemented state machines, and/or anycombination thereof for implementing the central server 104 is shown inFIG. 5. The machine readable instructions may be an executable programor portion of an executable program for execution by a computerprocessor such as the processor 712 shown in the example processorplatform 700 discussed below in connection with FIG. 7. The program maybe embodied in software stored on a non-transitory computer readablestorage medium such as a CD-ROM, a floppy disk, a hard drive, a DVD, aBlu-ray disk, or a memory associated with the processor 712, but theentire program and/or parts thereof could alternatively be executed by adevice other than the processor 712 and/or embodied in firmware ordedicated hardware. Further, although the example program is describedwith reference to the flowchart illustrated in FIG. 5, many othermethods of implementing the example central server 104 may alternativelybe used. For example, the order of execution of the blocks may bechanged, and/or some of the blocks described may be changed, eliminated,or combined. Additionally or alternatively, any or all of the blocks maybe implemented by one or more hardware circuits (e.g., discrete and/orintegrated analog and/or digital circuitry, an FPGA, an ASIC, acomparator, an operational-amplifier (op-amp), a logic circuit, etc.)structured to perform the corresponding operation without executingsoftware or firmware.

As mentioned above, the example processes of FIG. 5 may be implementedusing executable instructions (e.g., computer and/or machine readableinstructions) stored on a non-transitory computer and/or machinereadable medium such as a hard disk drive, a flash memory, a read-onlymemory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration for extended time periods,permanently, for brief instances, for temporarily buffering, and/or forcaching of the information). As used herein, the term non-transitorycomputer readable medium is expressly defined to include any type ofcomputer readable storage device and/or storage disk and to excludepropagating signals and to exclude transmission media.

“Including” and “comprising” (and all forms and tenses thereof) are usedherein to be open ended terms. Thus, whenever a claim employs any formof “include” or “comprise” (e.g., comprises, includes, comprising,including, having, etc.) as a preamble or within a claim recitation ofany kind, it is to be understood that additional elements, terms, etc.may be present without falling outside the scope of the correspondingclaim or recitation. As used herein, when the phrase “at least” is usedas the transition term in, for example, a preamble of a claim, it isopen-ended in the same manner as the term “comprising” and “including”are open ended. The term “and/or” when used, for example, in a form suchas A, B, and/or C refers to any combination or subset of A, B, C such as(1) A alone, (2) B alone, (3) C alone, (4) A with B, (5) A with C, (6) Bwith C, and (7) A with B and with C. As used herein in the context ofdescribing structures, components, items, objects and/or things, thephrase “at least one of A and B” is intended to refer to implementationsincluding any of (1) at least one A, (2) at least one B, and (3) atleast one A and at least one B. Similarly, as used herein in the contextof describing structures, components, items, objects and/or things, thephrase “at least one of A or B” is intended to refer to implementationsincluding any of (1) at least one A, (2) at least one B, and (3) atleast one A and at least one B. As used herein in the context ofdescribing the performance or execution of processes, instructions,actions, activities and/or steps, the phrase “at least one of A and B”is intended to refer to implementations including any of (1) at leastone A, (2) at least one B, and (3) at least one A and at least one B.Similarly, as used herein in the context of describing the performanceor execution of processes, instructions, actions, activities and/orsteps, the phrase “at least one of A or B” is intended to refer toimplementations including any of (1) at least one A, (2) at least one B,and (3) at least one A and at least one B.

Example machine readable instructions that may be executed to implementthe central server 104 of FIGS. 1 and/or 3 are illustrated in FIG. 5.With reference by example to FIG. 3, the example machine readableinstructions (e.g., process 500) of FIG. 5 begin when the workloadanalyzer 304 obtains message and alert data related to potential newproduct from the auditing device 102 (block 502). The example productanalyzer 304 determines if the product was previously identified byanother auditing device 102 (block 504). For example, the productanalyzer 306 obtains an indication from the workload analyzer 304 thatthe product is a part of another auditing device workload, or theproduct analyzer 306 identifies a reference product image in the imagedatabase 106 that matches the product image. If the example productanalyzer 306 determines that the product was previously identified(block 504), the alert authorizer 318 suppresses the message and alert(block 516). If the product analyzer 306 determines that the product hasnot been previously identified (block 504), the alert analyzer 312identifies an auditor profile associated with the auditing device 102(block 506). The alert analyzer 312 clusters the message and alert databased on characteristics associated with the auditor profile (block508). The alert analyzer 312 performs regression analysis to determine aprobability of transmitting the message and alert data to other auditingdevices 102 (block 510). The alert authorizer 318 determines whether theprobability satisfies a threshold (block 512). If the alert authorizer318 determines that the probability does not satisfy the threshold(block 512), the alert authorizer 318 suppresses the message and alert(block 516). If the alert authorizer 318 determines that the probabilitysatisfies the threshold (block 512), the alert authorizer 318 transmitsthe message and alert to other auditing devices 102 (block 514). In someexamples, the process 500 ends until another message and/or alert is tobe processed.

FIG. 7 is a block diagram of an example processor platform 700structured to execute the instructions of FIG. 5 to implement thecentral server 104 of FIGS. 1 and/or 3. The processor platform 700 canbe, for example, a server, a personal computer, a workstation, aself-learning machine (e.g., a neural network), a mobile device (e.g., acell phone, a smart phone, a tablet such as an iPad™), a personaldigital assistant (PDA), an Internet appliance, a DVD player, a CDplayer, a digital video recorder, a Blu-ray player, a gaming console, apersonal video recorder, a set top box, a headset or other wearabledevice, or any other type of computing device.

The processor platform 700 of the illustrated example includes aprocessor 712. The processor 712 of the illustrated example is hardware.For example, the processor 712 can be implemented by one or moreintegrated circuits, logic circuits, microprocessors, GPUs, DSPs, orcontrollers from any desired family or manufacturer. The hardwareprocessor may be a semiconductor based (e.g., silicon based) device. Inthis example, the processor 700 implements the example workloadauthorizer 302, the example workload analyzer 304, the example productanalyzer 306, the example message analyzer 308, the example alertanalyzer 312, the example alert authorizer 318, and the example productreference generator 320.

The processor 712 of the illustrated example includes a local memory 713(e.g., a cache). The processor 712 of the illustrated example is incommunication with a main memory including a volatile memory 714 and anon-volatile memory 716 via a bus 718. The volatile memory 714 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS® Dynamic Random Access Memory(RDRAM®) and/or any other type of random access memory device. Thenon-volatile memory 716 may be implemented by flash memory and/or anyother desired type of memory device. Access to the main memory 714, 716is controlled by a memory controller.

The processor platform 700 of the illustrated example also includes aninterface circuit 720. The interface circuit 720 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), a Bluetooth® interface, a near fieldcommunication (NFC) interface, and/or a PCI express interface.

In the illustrated example, one or more input devices 722 are connectedto the interface circuit 720. The input device(s) 722 permit(s) a userto enter data and/or commands into the processor 612. The inputdevice(s) can be implemented by, for example, an audio sensor, amicrophone, a camera (still or video), a keyboard, a button, a mouse, atouchscreen, a track-pad, a trackball, isopoint and/or a voicerecognition system.

One or more output devices 724 are also connected to the interfacecircuit 720 of the illustrated example. The output devices 724 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay (LCD), a cathode ray tube display (CRT), an in-place switching(IPS) display, a touchscreen, etc.), a tactile output device, a printerand/or speaker. The interface circuit 720 of the illustrated example,thus, typically includes a graphics driver card, a graphics driver chipand/or a graphics driver processor.

The interface circuit 720 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem, a residential gateway, a wireless access point, and/or a networkinterface to facilitate exchange of data with external machines (e.g.,computing devices of any kind) via a network 726. The communication canbe via, for example, an Ethernet connection, a digital subscriber line(DSL) connection, a telephone line connection, a coaxial cable system, asatellite system, a line-of-site wireless system, a cellular telephonesystem, etc.

The processor platform 700 of the illustrated example also includes oneor more mass storage devices 728 for storing software and/or data.Examples of such mass storage devices 728 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, redundantarray of independent disks (RAID) systems, and digital versatile disk(DVD) drives.

The machine executable instructions 732 of FIG. 5 may be stored in themass storage device 728, in the volatile memory 714, in the non-volatilememory 716, and/or on a removable non-transitory computer readablestorage medium such as a CD or DVD.

From the foregoing, it will be appreciated that example methods,apparatus, systems and articles of manufacture have been disclosed thatmonitor auditing devices to suppress or transmit alert data. Thedisclosed examples improve the efficiency of using a computing device bysuppressing alert data that is ineffective to reduce an amount ofresources (e.g., network, storage, computational, personnel, etc.)required for subsequent processing. The disclosed methods, apparatus andarticles of manufacture are accordingly directed to one or moreimprovement(s) in the functioning of a computer.

The following paragraphs provide various examples of the examplesdisclosed herein

Example 1 can be a workload analyzer to obtain alert data related to apotential new product from an auditing device, a product analyzer toidentify a product within the alert data to determine if the product hasbeen previously identified by another auditing device; in response todetermining that the product has not been previously identified, analert analyzer to: cluster the alert data based on characteristicsassociated with an auditor profile of the auditing device, and determinea probability of transmitting the alert data to other auditing devicesbased on the clustered alert data; and an alert authorizer to suppressthe alert data from being transmitted to the other auditing devices toreduce an amount of network resources required for subsequent processingwhen the probability does not satisfy a threshold.

Example 2 includes the apparatus of example 1, wherein the workloadanalyzer is to determine if the product is associated with anotherworkload of one of the other auditing devices, the workload analyzer totransmit the determination to the alert authorizer to suppress the alertdata from being transmitted.

Example 3 includes the apparatus of any one of examples 1-2, furtherincluding a message analyzer to: parse messages from e other auditingdevices to determine if the product has been previously identified, andtransmit an indication message to the product analyzer indicatingwhether or not the product has been identified in the messages from theother auditing devices.

Example 4 includes the apparatus of any one of examples 1-3, wherein thecharacteristics associated with the auditor profile of the auditingdevice include at least one of a number of years of experience, a skilllevel, an efficiency measure, an average amount of time to perform anaudit, an alert data generation measure, or a number of stock keepingunits (SKU).

Example 5 includes the apparatus of any one of examples 1-4, whereinincluding the alert analyzer is to perform a logistic regression todetermine the probability of transmitting the alert data to otherauditing devices.

Example 6 includes the apparatus of any one of examples 1-5, wherein thealert analyzer is to perform the logistic regression to learncoefficients associated with a cluster index.

Example 7 includes the apparatus of any one of examples 1-6, wherein thealert authorizer is to transmit the alert data to the other auditingdevices when the probability satisfies the threshold to reduce an amountof network resources required to process subsequent alert data.

Example 8 can be a non-transitory computer readable medium comprisinginstructions that, when executed, cause a machine to at least obtainalert data related to a potential new product from an auditing device,identify a product within the alert data to determine if the product hasbeen previously identified by another auditing device; in response todetermining that the product has not been previously identified: clusterthe alert data based on characteristics associated with an auditorprofile of the auditing device, and determine a probability oftransmitting the alert data to other auditing devices based on theclustered alert data, and suppress the alert data from being transmittedto the other auditing devices to reduce an amount of network resourcesrequired for subsequent processing when the probability does not satisfya threshold.

Example 9 includes the non-transitory computer readable medium ofexample 8, wherein the instructions further cause the machine todetermine if the product is associated with another workload of one ofthe other auditing devices, and transmit the determination to suppressthe alert data from being transmitted.

Example 10 includes the non-transitory computer readable medium of anyone of examples 8-9, wherein the instructions further cause the machineto: parse messages from the other auditing devices to determine if theproduct has been previously identified; and transmit an indicationmessage indicating whether or not the product has been identified in themessages from the other auditing devices.

Example 11 includes the non-transitory computer readable medium of anyone of examples 8-10, wherein the characteristics associated with theauditor profile of the auditing device include at least one of a numberof years of experience, a skill level, an efficiency measure, an averageamount of time to perform an audit, an alert data generation measure, ora number of stock keeping units (SKU).

Example 12 includes the non-transitory computer readable medium of anyone of examples 8-11, wherein the instructions further cause the machineto perform a logistic regression to determine the probability oftransmitting the alert data to other auditing devices.

Example 13 includes the non-transitory computer readable medium of anyone of examples 8-12, wherein the performing of the logistic regressionis to learn coefficients associated with a cluster index.

Example 14 includes the non-transitory computer readable medium of anyone of examples 8-13, wherein the instructions further cause the machineto transmit the alert data to the other auditing devices when theprobability satisfies the threshold to reduce an amount of networkresources required to process subsequent alert data.

Example 15 can be obtaining, by executing an instruction with aprocessor, alert data related to a potential new product from anauditing device, identifying, by executing an instruction with theprocessor, a product within the alert data to determine if the producthas been previously identified by another auditing device; in responseto determining that the product has not been previously identified:clustering, by executing an instruction with the processor, the alertdata based on characteristics associated with an auditor profile of theauditing device, and determining, by executing an instruction with theprocessor, a probability of transmitting the alert data to otherauditing devices based on the clustered alert data, and suppressing, byexecuting an instruction with the processor, the alert data from beingtransmitted to the other auditing devices to reduce an amount of networkresources required for subsequent processing when the probability doesnot satisfy a threshold.

Example 16 includes the method of example 15, further includingdetermining if the product is associated with another workload of one ofthe other auditing devices, and transmitting the determination tosuppress the alert data from being transmitted.

Example 17 includes the method of any one of examples 15-16, furtherincluding: parsing messages from the other auditing devices to determineif the product has been previously identified, and transmitting anindication message indicating whether or not the product has beenidentified in the messages from the other auditing devices.

Example 18 includes the method of any one of examples 15-17, wherein thecharacteristics associated with the auditor profile of the auditingdevice include at least one of a number of years of experience, a skilllevel, an efficiency measure, an average amount of time to perform anaudit, an alert data generation measure, or a number of stock keepingunits (SKU).

Example 19 includes the method of any one of examples 15-18, furtherincluding performing a logistic regression to determine the probabilityof transmitting the alert data to other auditing devices, the logisticregression to learn coefficients associated with a cluster index.

Example 20 includes the method of any one of examples 15-19, furtherincluding transmitting the alert data to the other auditing devices whenthe probability satisfies the threshold to reduce an amount of networkresources required to process subsequent alert data.

Example 21 can be means for analyzing a workload to obtain alert datarelated to a potential new product from an auditing device, means foranalyzing a product to identify a product within the alert data todetermine if the product has been previously identified by anotherauditing device; in response to determining that the product has notbeen previously identified, means for analyzing an alert to: cluster thealert data based on characteristics associated with an auditor profileof the auditing device, and determine a probability of transmitting thealert data to other auditing devices based on the clustered alert data,and means for authorizing an alert to suppress the alert data from beingtransmitted to the other auditing devices to reduce an amount of networkresources required for subsequent processing when the probability doesnot satisfy a threshold.

Example 22 includes the apparatus of example 21, wherein the workloadanalyzing means is to determine if the product is associated withanother workload of one of the other auditing devices, the workloadanalyzing means to transmit the determination to the alert authorizingmeans to suppress the alert data from being transmitted.

Example 23 includes the apparatus of any one of examples 21-22, furtherincluding means for analyzing a message to: parse messages from theother auditing devices to determine if the product has been previouslyidentified, and transmit an indication message to the product analyzingmeans to indicate whether or not the product has been identified in themessages from the other auditing devices.

Example 24 includes the apparatus of any one of examples 21-23, whereinthe characteristics associated with the auditor profile of the auditingdevice include at least one of a number of years of experience, a skilllevel, an efficiency measure, an average amount of time to perform anaudit, an alert data generation measure, or a number of stock keepingunits (SKU).

Example 25 includes the apparatus of any one of examples 21-24, whereinthe alert analyzing means is to perform a logistic regression todetermine the probability of transmitting the alert data to otherauditing devices.

Example 26 includes the apparatus of any one of examples 21-25, whereinthe alert analyzing means is to perform the logistic regression to learncoefficients associated with a cluster index.

Example 27 includes the apparatus of any one of examples 21-26, whereinthe alert authorizing means is to transmit the alert data to the otherauditing devices when the probability satisfies the threshold to reducean amount of network resources required to process subsequent alertdata.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. An apparatus comprising: product analyzingcircuitry to: identify a product represented by the alert data obtainedfrom a first auditing device; and determine if the product has beenpreviously identified; alert analyzing circuitry to, in response to adetermination that the product has not been previously identified:determine a first probability of transmitting the alert data to a secondauditing device, the first probability associated with a first cluster,the first cluster based on a first characteristic of a first auditorprofile of the first auditing device; and determine a second probabilityof transmitting the alert data to a third auditing device, the secondprobability associated with a second cluster, the second cluster basedon a second characteristic of the first auditor profile, the firstcharacteristic and the second characteristic independent of the alertdata, the first characteristic different from the second characteristic;and alert authorizing circuitry to: reduce an amount of networkresources required for subsequent processing by suppressing the alertdata from being transmitted to the second auditing device when the firstprobability does not satisfy a threshold, the second auditing deviceassociated with a second auditor profile sharing the firstcharacteristic with the first auditor profile; and cause transmission ofthe alert data to the third auditing device when the second probabilitysatisfies the threshold, the third auditing device associated with athird auditor profile sharing the second characteristic with the firstauditor profile.
 2. The apparatus of claim 1, further including workloadanalyzing circuitry to: determine if the product is associated with aworkload of one of the second auditing device or the third auditingdevice; and suppress transmission of the alert data to the secondauditing device and the third auditing device in response to the productbeing associated with the workload.
 3. The apparatus of claim 1, furtherincluding message analyzing circuitry to: parse a message from one ofthe second auditing device or the third auditing device to determine ifthe product has been previously identified; and indicate whether theproduct has been identified in the message from the one of the secondauditing device or the third auditing device.
 4. The apparatus of claim1, wherein one or more of the first characteristic or the secondcharacteristic includes at least one of a number of years of experience,a skill level, an efficiency measure, an average amount of time toperform an audit, or a number of stock keeping units (SKU).
 5. Theapparatus of claim 1, wherein the alert analyzing circuitry is to:determine the first probability via a first logistic regression; anddetermine the second probability via a second logistic regression. 6.The apparatus of claim 5, wherein the alert analyzing circuitry is to:determine, based on the first logistic regression, first coefficientsassociated with a first cluster index; and determine, based on thesecond logistic regression, second coefficients associated with a secondcluster index.
 7. The apparatus of claim 1, wherein the alertauthorizing circuitry is to cause a central server to transmit the alertdata to the third auditing device and a fourth auditing device when thesecond probability satisfies the threshold to reduce an amount ofnetwork resources required to process subsequent alert data, the fourthauditing device associated with a fourth auditor profile sharing thesecond characteristic with the first auditor profile.
 8. Anon-transitory computer readable medium comprising instructions that,when executed, cause at least one processor to at least: identify aproduct represented by alert data obtained from a first auditing device;determine if the product has been previously identified; in response toa determination that the product has not been previously identified:determine a first probability of transmitting the alert data to a secondauditing device, the first probability associated with a first cluster,the first cluster based on a first characteristic of a first auditorprofile of the first auditing device; and determine a second probabilityof transmitting the alert data to a third auditing device, the secondprobability associated with a second cluster, the second cluster basedon a second characteristic of the first auditor profile, the firstcharacteristic and the second characteristic independent of the alertdata, the first characteristic different from the second characteristic;reduce an amount of network resources required for subsequent processingby suppressing the alert data from being transmitted to the secondauditing device when the first probability does not satisfy a threshold,the second auditing device associated with a second auditor profilesharing the first characteristic with the first auditor profile; andcause transmission of the alert data to the third auditing device whenthe second probability satisfies the threshold, the third auditingdevice associated with a third auditor profile sharing the secondcharacteristic with the first auditor profile.
 9. The non-transitorycomputer readable medium of claim 8, wherein the instructions furthercause the at least one processor to: determine if the product isassociated with a workload of one of the second auditing device or thethird auditing device; and suppress transmission of the alert data tothe second auditing device and the third auditing device in response tothe product being associated with the workload.
 10. The non-transitorycomputer readable medium of claim 8, wherein the instructions furthercause the at least one processor to: parse a message from one of thesecond auditing device or the third auditing device to determine if theproduct has been previously identified; and indicate whether the producthas been identified in the message from the one of the second auditingdevice or the third auditing device.
 11. The non-transitory computerreadable medium of claim 8, wherein one or more of the firstcharacteristic or the second characteristic includes at least one of anumber of years of experience, a skill level, an efficiency measure, anaverage amount of time to perform an audit, or a number of stock keepingunits (SKU).
 12. The non-transitory computer readable medium of claim 8,wherein the instructions further cause the at least one processor to:determine the first probability via a first logistic regression; anddetermine the second probability via a second logistic regression. 13.The non-transitory computer readable medium of claim 12, wherein theinstructions further cause the at least one processor to: determine,based on the first logistic regression, first coefficients associatedwith a first cluster index; and determine, based on the second logisticregression, second coefficients associated with a second cluster index.14. The non-transitory computer readable medium of claim 8, wherein theinstructions further cause the at least one processor to cause a centralserver to transmit the alert data to the third auditing device and afourth auditing device when the second probability satisfies thethreshold to reduce an amount of network resources required to processsubsequent alert data, the fourth auditing device associated with afourth auditor profile sharing the second characteristic with the firstauditor profile.
 15. A method comprising: identifying, by executing aninstruction with a processor, a product represented by alert dataobtained from a first auditing device; determining, by executing aninstruction with the processor, if the product has been previouslyidentified; in response to determining that the product has not beenpreviously identified: determining, by executing an instruction with theprocessor, a first probability of transmitting the alert data to asecond auditing device, the first probability associated with a firstcluster, the first cluster based on a first characteristic of a firstauditor profile of the first auditing device; and determining, byexecuting an instruction with the processor, a second probability oftransmitting the alert data to a third auditing device, the secondprobability associate with a second cluster, the second cluster based ona second characteristic of the first auditor profile, the firstcharacteristic and the second characteristic independent of the alertdata, the first characteristic different from the second characteristic;reducing an amount of network resources required for subsequentprocessing by suppressing, by executing an instruction with theprocessor, the alert data from being transmitted to the second auditingdevice when the first probability does not satisfy a threshold, thesecond auditing device associated with a second auditor profile sharingthe first characteristic with the first auditor profile; and causingtransmission of, by executing an instruction with the processor, thealert data to the third auditing device when the second probabilitysatisfies the threshold, the third auditing device associated with athird auditor profile sharing the second characteristic with the firstauditor profile.
 16. The method of claim 15, further including:determining if the product is associated with a workload of one of thesecond auditing device or the third auditing device; and suppressingtransmission of the alert data to the second auditing device and thethird auditing device in response to the product being associated withthe workload.
 17. The method of claim 15, further including: parsing amessage from one of the second auditing device or the third auditingdevice to determine if the product has been previously identified; andcausing transmission of an indication message indicating whether theproduct has been identified in the message from the one of the secondauditing device or the third auditing device.
 18. The method of claim15, wherein one or more of the first characteristic or the secondcharacteristic includes at least one of a number of years of experience,a skill level, an efficiency measure, an average amount of time toperform an audit, or a number of stock keeping units (SKU).
 19. Themethod of claim 15, further including: determining the first probabilityvia a first logistic regression; determining, from the first logisticregression, first coefficients associated with a first cluster index;determining the second probability via a second logistic regression; anddetermining, from the second logistic regression, second coefficientsassociated with a second cluster index.
 20. The method of claim 15,further including transmitting, via a central server, the alert data tothe third auditing device and a fourth auditing device when the secondprobability satisfies the threshold to reduce an amount of networkresources required to process subsequent alert data, the fourth auditingdevice associated with a fourth auditor profile sharing the secondcharacteristic with the first auditor profile.
 21. An apparatuscomprising: at least one memory; instructions; and processor circuitryto execute the instructions to: identify a product represented by alertdata obtained from a first auditing device; determine if the product hasbeen previously identified devices; in response to a determination thatthe product has not been previously identified: determine a firstprobability of transmitting the alert data to a second auditing device,the first probability associated with a first cluster, the first clusterbased on a first characteristic of a first auditor profile of the firstauditing device; and determine a second probability of transmitting thealert data to a third auditing device, the second probability associatedwith a second cluster, the second cluster based on a secondcharacteristic of the first auditor profile, the first characteristicand the second characteristic independent of the alert data, the firstcharacteristic different from the second characteristic; reduce anamount of network resources required for subsequent processing bysuppressing the alert data from being transmitted to the second auditingdevice when the first probability does not satisfy a threshold, thesecond auditing device associated with a second auditor profile sharingthe first characteristic with the first auditor profile; and causetransmission of the alert data to the third auditing device when thesecond probability satisfies the threshold, the third auditing deviceassociated with a third auditing profile sharing the secondcharacteristic with the first auditor profile.
 22. The apparatus ofclaim 21, wherein the processor circuitry is to: determine if theproduct is associated with a workload of one of the second auditingdevice or the third auditing device; and suppress transmission of thealert data to the second auditing device and the third auditing devicein response to the product being associated with the workload.
 23. Theapparatus of claim 21, wherein the processor circuitry is to: parse amessage from one of the second auditing device or the third auditingdevice to determine if the product has been previously identified; andindicate whether the product has been identified in the message from theone of the second auditing device or the third auditing device.
 24. Theapparatus of claim 21, wherein one or more of the first characteristicor the second characteristic includes at least one of a number of yearsof experience, a skill level, an efficiency measure, an average amountof time to perform an audit, or a number of stock keeping units (SKU).25. The apparatus of claim 21, wherein the processor circuitry is to:determine the first probability via a first logistic regression; anddetermine the second probability via a second logistic regression. 26.The apparatus of claim 25, wherein the processor circuitry is to:determine, from the first logistic regression, first coefficientsassociated with a first cluster index; and determine, from the secondlogistic regression, second coefficients associated with a secondcluster index.
 27. The apparatus of claim 21, wherein the processorcircuitry is to cause a central server to transmit the alert data to thethird auditing device and a fourth auditing device when the secondprobability satisfies the threshold to reduce an amount of networkresources required to process subsequent alert data, the fourth auditingdevice associated with a fourth auditor profile sharing the secondcharacteristic with the first auditor profile.
 28. The apparatus ofclaim 1, wherein the product analyzing circuitry is to: determine if afirst image included in the alert data matches a second image includedin a database of reference images; and indicate whether the product hasbeen identified based on whether the first image matches the secondimage.
 29. The non-transitory computer readable medium of claim 8,wherein the instructions further cause the at least one processor to:determine if a first image included in the alert data matches a secondimage included in a database of reference images; and indicate whetherthe product has been identified based on whether the first image matchesthe second image.
 30. The method of claim 15, further including:determining if a first image included in the alert data matches a secondimage included in a database of reference images; and indicating whetherthe product has been identified based on whether the first image matchesthe second image.
 31. The apparatus of claim 21, wherein the processorcircuitry is to: determine if a first image included in the alert datamatches a second image included in a database of reference images; andindicate whether the product has been identified based on whether thefirst image matches the second image.